From 9b25f5d37a78e9a28134b917c2768849f529cd6e Mon Sep 17 00:00:00 2001
From: Nate George <wordsforthewise@gmail.com>
Date: Wed, 27 Jul 2016 14:58:25 -0600
Subject: [PATCH] Fix docs links (#7005)
* docs: fix broken and redirect links
see #7000
* docs: fix links
see #7000
* docs: merge with master
* docs: fix fnrs and tinyclues logo links
* docs: fix link reference in text
* docs: fix typo
* docs: added back in metaoptimize-qa paragraph
* docs: update language for defunct site
* docs: update stackexchange section
* docs: remove defunct site, move quora to top
* docs: remove defunct link and rearrange links
---
AUTHORS.rst | 18 ++++++++---------
CONTRIBUTING.md | 4 ++--
doc/about.rst | 20 +++++++++----------
doc/datasets/olivetti_faces.rst | 8 +++-----
doc/developers/advanced_installation.rst | 8 ++++----
doc/developers/contributing.rst | 18 ++++++++---------
doc/faq.rst | 2 +-
doc/modules/clustering.rst | 4 ++--
doc/modules/cross_validation.rst | 2 +-
doc/modules/feature_extraction.rst | 2 +-
doc/modules/feature_selection.rst | 4 ++--
doc/modules/manifold.rst | 2 +-
doc/modules/model_persistence.rst | 2 +-
doc/modules/naive_bayes.rst | 2 +-
doc/modules/svm.rst | 2 +-
doc/modules/tree.rst | 2 +-
doc/presentations.rst | 2 +-
doc/related_projects.rst | 8 ++++----
doc/support.rst | 4 ++--
doc/testimonials/testimonials.rst | 10 ++++------
.../statistical_inference/finding_help.rst | 17 +++++-----------
examples/neighbors/plot_species_kde.py | 2 +-
sklearn/cluster/birch.py | 2 +-
sklearn/datasets/olivetti_faces.py | 6 +++---
sklearn/datasets/species_distributions.py | 8 ++++----
sklearn/decomposition/online_lda.py | 4 ++--
sklearn/feature_selection/mutual_info_.py | 4 ++--
sklearn/gaussian_process/gaussian_process.py | 2 +-
sklearn/kernel_approximation.py | 2 +-
sklearn/linear_model/least_angle.py | 2 +-
sklearn/random_projection.py | 4 ++--
31 files changed, 83 insertions(+), 94 deletions(-)
diff --git a/AUTHORS.rst b/AUTHORS.rst
index 56d629e890..40140feb1c 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -33,23 +33,23 @@ The following people have been core contributors to scikit-learn's development a
* Edouard Duchesnay
* `Tom Dupré la Tour <https://github.com/TomDLT>`_
* Alexander Fabisch
- * `Virgile Fritsch <http://parietal.saclay.inria.fr/Members/virgile-fritsch>`_
+ * `Virgile Fritsch <https://team.inria.fr/parietal/vfritsch/>`_
* `Satra Ghosh <http://www.mit.edu/~satra>`_
* `Angel Soler Gollonet <http://webylimonada.com>`_
* Chris Filo Gorgolewski
* `Alexandre Gramfort <http://alexandre.gramfort.net>`_
- * `Olivier Grisel <http://twitter.com/ogrisel>`_
+ * `Olivier Grisel <https://twitter.com/ogrisel>`_
* `Jaques Grobler <https://github.com/jaquesgrobler>`_
* `Yaroslav Halchenko <http://www.onerussian.com/>`_
- * `Brian Holt <http://info.ee.surrey.ac.uk/Personal/B.Holt/>`_
+ * `Brian Holt <http://personal.ee.surrey.ac.uk/Personal/B.Holt/>`_
* `Arnaud Joly <http://www.ajoly.org>`_
* Thouis (Ray) Jones
* `Kyle Kastner <http://kastnerkyle.github.io>`_
* `Manoj Kumar <https://manojbits.wordpress.com>`_
* Robert Layton
- * `Wei Li <http://kuantkid.github.com>`_
+ * `Wei Li <http://kuantkid.github.io/>`_
* Paolo Losi
- * `Gilles Louppe <http://www.montefiore.ulg.ac.be/~glouppe>`_
+ * `Gilles Louppe <http://glouppe.github.io/>`_
* `Jan Hendrik Metzen <https://github.com/jmetzen>`_
* Vincent Michel
* Jarrod Millman
@@ -57,10 +57,10 @@ The following people have been core contributors to scikit-learn's development a
* `Vlad Niculae <http://vene.ro>`_
* `Joel Nothman <http://joelnothman.com>`_
* `Alexandre Passos <http://atpassos.posterous.com>`_
- * `Fabian Pedregosa <http://fseoane.net/blog/>`_
- * `Peter Prettenhofer <http://sites.google.com/site/peterprettenhofer/>`_
+ * `Fabian Pedregosa <http://fa.bianp.net/blog/>`_
+ * `Peter Prettenhofer <https://sites.google.com/site/peterprettenhofer/>`_
* Bertrand Thirion
- * `Jake VanderPlas <http://www.astro.washington.edu/users/vanderplas/>`_
+ * `Jake VanderPlas <http://staff.washington.edu/jakevdp/>`_
* Nelle Varoquaux
- * `Gael Varoquaux <http://gael-varoquaux.info/blog/>`_
+ * `Gael Varoquaux <http://gael-varoquaux.info/>`_
* Ron Weiss
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 286c734a9d..5f6115e1c3 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -53,7 +53,7 @@ Click the 'Pull request' button to send your changes to the project's maintainer
review. This will send an email to the committers.
(If any of the above seems like magic to you, please look up the
-[Git documentation](http://git-scm.com/documentation) on the web, or ask a friend or another contributor for help.)
+[Git documentation](https://git-scm.com/documentation) on the web, or ask a friend or another contributor for help.)
Pull Request Checklist
----------------------
@@ -215,7 +215,7 @@ be placed in ``_build/html/`` and are viewable in a web browser. See the
For building the documentation, you will need
[sphinx](http://sphinx.pocoo.org/),
[matplotlib](http://matplotlib.sourceforge.net/), and
-[pillow](http://pillow.readthedocs.org/en/latest/).
+[pillow](http://pillow.readthedocs.io/en/latest/).
When you are writing documentation, it is important to keep a good
compromise between mathematical and algorithmic details, and give
diff --git a/doc/about.rst b/doc/about.rst
index dd3e8029e4..a6e8db8bae 100644
--- a/doc/about.rst
+++ b/doc/about.rst
@@ -113,7 +113,7 @@ The `PSF <https://www.python.org/psf/>`_ helped find and manage funding for our
2011 Granada sprint. More information can be found `here
<https://github.com/scikit-learn/scikit-learn/wiki/Past-sprints#granada-19th-21th-dec-2011>`__
-`tinyclues <http://www.tinyclues.com/>`_ funded the 2011 international Granada
+`tinyclues <https://www.tinyclues.com/>`_ funded the 2011 international Granada
sprint.
@@ -126,7 +126,7 @@ the *Paypal* button below or the `NumFOCUS Donations Page <http://www.numfocus.o
All donations will be handled by `NumFOCUS
<http://www.numfocus.org>`_, a non-profit-organization which is
managed by a board of `Scipy community members
-<http://www.numfocus.org/board>`_. NumFOCUS's mission is to foster
+<http://www.numfocus.org/board.html>`_. NumFOCUS's mission is to foster
scientific computing software, in particular in Python. As a fiscal home
of scikit-learn, it ensures that money is available when needed to keep
the project funded and available while in compliance with tax regulations.
@@ -176,21 +176,21 @@ The 2013 Paris international sprint
:target: http://www.telecom-paristech.fr/
-.. |tinyclues| image:: http://www.tinyclues.com/static/img/logo.png
+.. |tinyclues| image:: https://www.tinyclues.com/web/wp-content/uploads/2016/06/Tinyclues-PNG-logo.png
:width: 120pt
- :target: http://www.tinyclues.com/
+ :target: https://www.tinyclues.com/
-.. |afpy| image:: http://www.afpy.org/logo.png
+.. |afpy| image:: https://www.afpy.org/logo.png
:width: 120pt
- :target: http://www.afpy.org
+ :target: https://www.afpy.org
.. |SGR| image:: http://www.svi.cnrs-bellevue.fr/wikimedia/images/Logo_svi_inp.png
:width: 120pt
:target: http://www.svi.cnrs-bellevue.fr
-.. |FNRS| image:: http://www.fnrs.be/uploaddocs/images/COMMUNIQUER/FRS-FNRS_rose_transp.png
+.. |FNRS| image:: http://www.fnrs.be/en/images/FRS-FNRS_rose_transp.png
:width: 120pt
:target: http://www.frs-fnrs.be/
@@ -211,13 +211,13 @@ The 2013 Paris international sprint
Infrastructure support
----------------------
-- We would like to thank `Rackspace <http://www.rackspace.com>`_ for providing
- us with a free `Rackspace Cloud <http://www.rackspace.com/cloud/>`_ account to
+- We would like to thank `Rackspace <https://www.rackspace.com>`_ for providing
+ us with a free `Rackspace Cloud <https://www.rackspace.com/cloud/>`_ account to
automatically build the documentation and the example gallery from for the
development version of scikit-learn using `this tool
<https://github.com/scikit-learn/sklearn-docbuilder>`_.
- We would also like to thank `Shining Panda
- <https://www.shiningpanda-ci.com/>`_ for free CPU time on their Continuous
+ <http://shiningpanda.com/>`_ for free CPU time on their Continuous
Integration server.
diff --git a/doc/datasets/olivetti_faces.rst b/doc/datasets/olivetti_faces.rst
index 4c774f75ed..19c5601f7c 100644
--- a/doc/datasets/olivetti_faces.rst
+++ b/doc/datasets/olivetti_faces.rst
@@ -5,15 +5,13 @@ The Olivetti faces dataset
==========================
-This dataset contains a set of face images taken between April 1992 and April
-1994 at AT&T Laboratories Cambridge. The website describing the original
-dataset is now defunct, but archived copies can be accessed through
-`the Internet Archive's Wayback Machine`_. The
+`This dataset contains a set of face images`_ taken between April 1992 and April
+1994 at AT&T Laboratories Cambridge. The
:func:`sklearn.datasets.fetch_olivetti_faces` function is the data
fetching / caching function that downloads the data
archive from AT&T.
-.. _the Internet Archive's Wayback Machine: http://wayback.archive.org/web/*/http://www.uk.research.att.com/facedatabase.html
+.. _This dataset contains a set of face images: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
As described on the original website:
diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst
index 84a4e82adb..c086a3deb3 100644
--- a/doc/developers/advanced_installation.rst
+++ b/doc/developers/advanced_installation.rst
@@ -168,7 +168,7 @@ first, you need to install `numpy <http://www.numpy.org/>`_ and `scipy
wheel packages (.whl files) for scikit-learn from `pypi
<https://pypi.python.org/pypi/scikit-learn/>`_ can be installed with the `pip
-<https://pip.readthedocs.org/en/stable/installing/>`_ utility.
+<https://pip.readthedocs.io/en/stable/installing/>`_ utility.
open a console and type the following to install or upgrade scikit-learn to the
latest stable release::
@@ -279,7 +279,7 @@ path environment variable.
-------------
for 32-bit python it is possible use the standalone installers for
-`microsoft visual c++ express 2008 <http://go.microsoft.com/?linkid=7729279>`_
+`microsoft visual c++ express 2008 <http://download.microsoft.com/download/A/5/4/A54BADB6-9C3F-478D-8657-93B3FC9FE62D/vcsetup.exe>`_
for python 2 or microsoft visual c++ express 2010 for python 3.
once installed you should be able to build scikit-learn without any
@@ -301,7 +301,7 @@ as a new drive with a ``setup.exe`` installer in it.
- for python 2 you need sdk **v7.0**: `ms windows sdk for windows 7 and .net
framework 3.5 sp1
- <http://www.microsoft.com/en-us/download/details.aspx?id=18950>`_
+ <https://www.microsoft.com/en-us/download/details.aspx?id=18950>`_
- for python 3 you need sdk **v7.1**: `ms windows sdk for windows 7 and .net
framework 4
@@ -377,7 +377,7 @@ testing scikit-learn once installed
-----------------------------------
testing requires having the `nose
-<https://nose.readthedocs.org/en/latest/>`_ library. after
+<https://nose.readthedocs.io/en/latest/>`_ library. after
installation, the package can be tested by executing *from outside* the
source directory::
diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst
index dd46325639..512a340785 100644
--- a/doc/developers/contributing.rst
+++ b/doc/developers/contributing.rst
@@ -28,7 +28,7 @@ also welcome to post feature requests or pull requests.
Retrieving the latest code
==========================
-We use `Git <http://git-scm.com/>`_ for version control and
+We use `Git <https://git-scm.com/>`_ for version control and
`GitHub <https://github.com/>`_ for hosting our main repository.
You can check out the latest sources with the command::
@@ -50,7 +50,7 @@ extension in place::
Another option is to use the ``develop`` option if you change your code a lot
and do not want to have to reinstall every time. This basically builds the
extension in place and creates a link to the development directory (see
-`the setuptool docs <https://pythonhosted.org/setuptools/setuptools.html#development-mode>`_)::
+`the setuptool docs <http://setuptools.readthedocs.io/en/latest/setuptools.html#development-mode>`_)::
python setup.py develop
@@ -129,7 +129,7 @@ visibility.
$ git remote add upstream https://github.com/scikit-learn/scikit-learn.git
(If any of the above seems like magic to you, then look up the
-`Git documentation <http://git-scm.com/documentation>`_ on the web.)
+`Git documentation <https://git-scm.com/documentation>`_ on the web.)
Contributing pull requests
--------------------------
@@ -225,7 +225,7 @@ and Cython optimizations.
workflow, please pay a visit to the `Scipy Development Workflow
<http://docs.scipy.org/doc/numpy/dev/gitwash/development_workflow.html>`_ -
and the `Astropy Workflow for Developers
- <http://astropy.readthedocs.org/en/latest/development/workflow/development_workflow.html>`_
+ <http://astropy.readthedocs.io/en/latest/development/workflow/development_workflow.html>`_
sections.
.. _filing_bugs:
@@ -299,9 +299,9 @@ and are viewable in a web browser. See the README file in the doc/ directory
for more information.
For building the documentation, you will need `sphinx
-<http://sphinx-doc.org/>`_,
+<http://www.sphinx-doc.org/en/stable/>`_,
`matplotlib <http://matplotlib.org>`_ and
-`pillow <http://pillow.readthedocs.org/en/latest/>`_.
+`pillow <http://pillow.readthedocs.io/en/latest/>`_.
**When you are writing documentation**, it is important to keep a good
compromise between mathematical and algorithmic details, and give
@@ -361,7 +361,7 @@ Testing and improving test coverage
High-quality `unit testing <https://en.wikipedia.org/wiki/Unit_testing>`_
is a corner-stone of the scikit-learn development process. For this
-purpose, we use the `nose <http://nose.readthedocs.org/en/latest/>`_
+purpose, we use the `nose <http://nose.readthedocs.io/en/latest/>`_
package. The tests are functions appropriately named, located in `tests`
subdirectories, that check the validity of the algorithms and the
different options of the code.
@@ -480,7 +480,7 @@ In addition, we add the following guidelines:
It makes the code harder to read as the origin of symbols is no
longer explicitly referenced, but most important, it prevents
using a static analysis tool like `pyflakes
- <http://www.divmod.org/trac/wiki/DivmodPyflakes>`_ to automatically
+ <https://divmod.readthedocs.io/en/latest/products/pyflakes.html>`_ to automatically
find bugs in scikit-learn.
* Use the `numpy docstring standard
@@ -489,7 +489,7 @@ In addition, we add the following guidelines:
A good example of code that we like can be found `here
-<https://svn.enthought.com/enthought/browser/sandbox/docs/coding_standard.py>`_.
+<https://gist.github.com/nateGeorge/5455d2c57fb33c1ae04706f2dc4fee01>`_.
Input validation
----------------
diff --git a/doc/faq.rst b/doc/faq.rst
index 62471a2e23..eaf674d70d 100644
--- a/doc/faq.rst
+++ b/doc/faq.rst
@@ -102,7 +102,7 @@ fix bugs, maintain code and review contributions.
Any algorithm that is added needs future attention by the developers,
at which point the original author might long have lost interest.
Also see `this thread on the mailing list
-<http://sourceforge.net/p/scikit-learn/mailman/scikit-learn-general/thread/CAAkaFLWcBG%2BgtsFQzpTLfZoCsHMDv9UG5WaqT0LwUApte0TVzg%40mail.gmail.com/#msg33104380>`_.
+<https://sourceforge.net/p/scikit-learn/mailman/scikit-learn-general/thread/CAAkaFLWcBG+gtsFQzpTLfZoCsHMDv9UG5WaqT0LwUApte0TVzg@mail.gmail.com/#msg33104380>`_.
Why did you remove HMMs from scikit-learn?
--------------------------------------------
diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
index 372ae5aa5a..e0264da054 100644
--- a/doc/modules/clustering.rst
+++ b/doc/modules/clustering.rst
@@ -424,7 +424,7 @@ works well for a small number of clusters but is not advised when using
many clusters.
For two clusters, it solves a convex relaxation of the `normalised
-cuts <http://www.cs.berkeley.edu/~malik/papers/SM-ncut.pdf>`_ problem on
+cuts <http://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf>`_ problem on
the similarity graph: cutting the graph in two so that the weight of the
edges cut is small compared to the weights of the edges inside each
cluster. This criteria is especially interesting when working on images:
@@ -876,7 +876,7 @@ the user is advised
* Roberto Perdisci
JBirch - Java implementation of BIRCH clustering algorithm
- https://code.google.com/p/jbirch/
+ https://code.google.com/archive/p/jbirch
.. _clustering_evaluation:
diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst
index 92be9e6e9e..4336e38749 100644
--- a/doc/modules/cross_validation.rst
+++ b/doc/modules/cross_validation.rst
@@ -343,7 +343,7 @@ fold cross validation should be preferred to LOO.
* R. Kohavi, `A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
<http://web.cs.iastate.edu/~jtian/cs573/Papers/Kohavi-IJCAI-95.pdf>`_, Intl. Jnt. Conf. AI
* R. Bharat Rao, G. Fung, R. Rosales, `On the Dangers of Cross-Validation. An Experimental Evaluation
- <http://www.siam.org/proceedings/datamining/2008/dm08_54_Rao.pdf>`_, SIAM 2008;
+ <http://people.csail.mit.edu/romer/papers/CrossVal_SDM08.pdf>`_, SIAM 2008;
* G. James, D. Witten, T. Hastie, R Tibshirani, `An Introduction to
Statistical Learning <http://www-bcf.usc.edu/~gareth/ISL>`_, Springer 2013.
diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst
index c0cfa5d318..c01d726804 100644
--- a/doc/modules/feature_extraction.rst
+++ b/doc/modules/feature_extraction.rst
@@ -210,7 +210,7 @@ otherwise the features will not be mapped evenly to the columns.
Josh Attenberg (2009). `Feature hashing for large scale multitask learning
<http://alex.smola.org/papers/2009/Weinbergeretal09.pdf>`_. Proc. ICML.
- * `MurmurHash3 <http://code.google.com/p/smhasher/wiki/MurmurHash3>`_.
+ * `MurmurHash3 <https://github.com/aappleby/smhasher>`_.
.. _text_feature_extraction:
diff --git a/doc/modules/feature_selection.rst b/doc/modules/feature_selection.rst
index 2d944f56e9..7826cb923f 100644
--- a/doc/modules/feature_selection.rst
+++ b/doc/modules/feature_selection.rst
@@ -283,11 +283,11 @@ of features non zero.
.. topic:: References:
.. [B2009] F. Bach, "Model-Consistent Sparse Estimation through the
- Bootstrap." http://hal.inria.fr/hal-00354771/
+ Bootstrap." https://hal.inria.fr/hal-00354771/
.. [M2010] N. Meinshausen, P. Buhlmann, "Stability selection",
Journal of the Royal Statistical Society, 72 (2010)
- http://arxiv.org/pdf/0809.2932
+ http://arxiv.org/pdf/0809.2932.pdf
Tree-based feature selection
----------------------------
diff --git a/doc/modules/manifold.rst b/doc/modules/manifold.rst
index 894da9e7d4..c697fe76d8 100644
--- a/doc/modules/manifold.rst
+++ b/doc/modules/manifold.rst
@@ -146,7 +146,7 @@ The overall complexity of Isomap is
.. topic:: References:
* `"A global geometric framework for nonlinear dimensionality reduction"
- <http://www.sciencemag.org/content/290/5500/2319.full>`_
+ <http://science.sciencemag.org/content/290/5500/2319.full>`_
Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. Science 290 (5500)
.. _locally_linear_embedding:
diff --git a/doc/modules/model_persistence.rst b/doc/modules/model_persistence.rst
index a87688bb4c..8f8bc4a620 100644
--- a/doc/modules/model_persistence.rst
+++ b/doc/modules/model_persistence.rst
@@ -14,7 +14,7 @@ Persistence example
-------------------
It is possible to save a model in the scikit by using Python's built-in
-persistence model, namely `pickle <http://docs.python.org/2/library/pickle.html>`_::
+persistence model, namely `pickle <https://docs.python.org/2/library/pickle.html>`_::
>>> from sklearn import svm
>>> from sklearn import datasets
diff --git a/doc/modules/naive_bayes.rst b/doc/modules/naive_bayes.rst
index dea0de3748..b2e6f63018 100644
--- a/doc/modules/naive_bayes.rst
+++ b/doc/modules/naive_bayes.rst
@@ -71,7 +71,7 @@ it is known to be a bad estimator, so the probability outputs from
.. topic:: References:
* H. Zhang (2004). `The optimality of Naive Bayes.
- <http://www.cs.unb.ca/profs/hzhang/publications/FLAIRS04ZhangH.pdf>`_
+ <http://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf>`_
Proc. FLAIRS.
.. _gaussian_naive_bayes:
diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst
index bdbb4f712a..7e2d8b0dba 100644
--- a/doc/modules/svm.rst
+++ b/doc/modules/svm.rst
@@ -619,7 +619,7 @@ term :math:`\rho` :
* `"Support-vector networks"
- <http://www.springerlink.com/content/k238jx04hm87j80g/>`_,
+ <http://link.springer.com/article/10.1007%2FBF00994018>`_,
C. Cortes, V. Vapnik - Machine Learning, 20, 273-297 (1995).
diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst
index cd355c8e4f..07cc8ac578 100644
--- a/doc/modules/tree.rst
+++ b/doc/modules/tree.rst
@@ -410,7 +410,7 @@ and threshold that yield the largest information gain at each node.
scikit-learn uses an optimised version of the CART algorithm.
.. _ID3: https://en.wikipedia.org/wiki/ID3_algorithm
-.. _CART: https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees
+.. _CART: https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees_.28CART.29
.. _tree_mathematical_formulation:
diff --git a/doc/presentations.rst b/doc/presentations.rst
index 52977d3daa..8b5d3bdc89 100644
--- a/doc/presentations.rst
+++ b/doc/presentations.rst
@@ -9,7 +9,7 @@ New to Scientific Python?
==========================
For those that are still new to the scientific Python ecosystem, we highly
recommend the `Python Scientific Lecture Notes
-<http://scipy-lectures.org>`_. This will help you find your footing a
+<http://www.scipy-lectures.org/>`_. This will help you find your footing a
bit and will definitely improve your scikit-learn experience. A basic
understanding of NumPy arrays is recommended to make the most of scikit-learn.
diff --git a/doc/related_projects.rst b/doc/related_projects.rst
index 99c6e7b046..d2c3ebbfca 100644
--- a/doc/related_projects.rst
+++ b/doc/related_projects.rst
@@ -22,11 +22,11 @@ enhance the functionality of scikit-learn's estimators.
scikit-learn pipelines and pandas data frame with dedicated transformers.
- `Scikit-Learn Laboratory
- <https://skll.readthedocs.org/en/latest/index.html>`_ A command-line
+ <https://skll.readthedocs.io/en/latest/index.html>`_ A command-line
wrapper around scikit-learn that makes it easy to run machine learning
experiments with multiple learners and large feature sets.
-- `auto-sklearn <https://github.com/automl/auto-sklearn/blob/master/source/index.rst>`_
+- `auto-sklearn <https://github.com/automl/auto-sklearn/>`_
An automated machine learning toolkit and a drop-in replacement for a
scikit-learn estimator
@@ -58,7 +58,7 @@ and tasks.
- `sklearn_theano <http://sklearn-theano.github.io/>`_ scikit-learn compatible
estimators, transformers, and datasets which use Theano internally
-- `lightning <http://www.mblondel.org/lightning/>`_ Fast state-of-the-art
+- `lightning <https://github.com/scikit-learn-contrib/lightning>`_ Fast state-of-the-art
linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc...).
- `Seqlearn <https://github.com/larsmans/seqlearn>`_ Sequence classification
@@ -73,7 +73,7 @@ and tasks.
- `pomegranate <https://github.com/jmschrei/pomegranate>`_ Probabilistic modelling
for Python, with an emphasis on hidden Markov models.
-- `py-earth <https://github.com/jcrudy/py-earth>`_ Multivariate adaptive
+- `py-earth <https://github.com/scikit-learn-contrib/py-earth>`_ Multivariate adaptive
regression splines
- `sklearn-compiledtrees <https://github.com/ajtulloch/sklearn-compiledtrees/>`_
diff --git a/doc/support.rst b/doc/support.rst
index eee09092ea..20f75cac60 100644
--- a/doc/support.rst
+++ b/doc/support.rst
@@ -28,7 +28,7 @@ User questions
tag.
- For general theoretical or methodological Machine Learning questions
- `metaoptimize.com/qa <http://metaoptimize.com/qa>`_ is probably a more
+ `stack exchange <http://stats.stackexchange.com/>`_ is probably a more
suitable venue.
In both cases please use a descriptive question in the title field (e.g.
@@ -98,4 +98,4 @@ versions can be found here:
* `0.15 <http://scikit-learn.org/0.15/>`_
Printable pdf documentation for all versions can be found `here
-<http://sourceforge.net/projects/scikit-learn/files/documentation/>`_.
+<https://sourceforge.net/projects/scikit-learn/files/documentation/>`_.
diff --git a/doc/testimonials/testimonials.rst b/doc/testimonials/testimonials.rst
index adb43fd364..161f85d9b1 100644
--- a/doc/testimonials/testimonials.rst
+++ b/doc/testimonials/testimonials.rst
@@ -64,7 +64,7 @@ Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spoti
At INRIA, we use scikit-learn to support leading-edge basic research in many
teams: `Parietal <https://team.inria.fr/parietal/>`_ for neuroimaging, `Lear
<http://lear.inrialpes.fr/>`_ for computer vision, `Visages
-<https://www.irisa.fr/visages/index>`_ for medical image analysis, `Privatics
+<https://team.inria.fr/visages/>`_ for medical image analysis, `Privatics
<https://team.inria.fr/privatics>`_ for security. The project is a fantastic
tool to address difficult applications of machine learing in an academic
environment as it is performant and versatile, but all easy-to-use and well
@@ -292,8 +292,6 @@ Greg Lamp, Co-founder Yhat
.. raw:: html
</span>
-
-`Rangespan <https://www.rangespan.com>`_
------------------------------------------
.. raw:: html
@@ -504,7 +502,7 @@ Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe
.. image:: images/peerindex.png
:width: 120pt
- :target: http://www.peerindex.com/
+ :target: https://www.brandwatch.com/peerindex-and-brandwatch
.. raw:: html
@@ -531,7 +529,7 @@ Ferenc Huszar - Senior Data Scientist at Peerindex
</span>
-`DataRobot <http://www.datarobot.com>`_
+`DataRobot <https://www.datarobot.com>`_
----------------------------------------
.. raw:: html
@@ -540,7 +538,7 @@ Ferenc Huszar - Senior Data Scientist at Peerindex
.. image:: images/datarobot.png
:width: 120pt
- :target: http://www.datarobot.com
+ :target: https://www.datarobot.com
.. raw:: html
diff --git a/doc/tutorial/statistical_inference/finding_help.rst b/doc/tutorial/statistical_inference/finding_help.rst
index 3dc1e3215e..9d73929fa7 100644
--- a/doc/tutorial/statistical_inference/finding_help.rst
+++ b/doc/tutorial/statistical_inference/finding_help.rst
@@ -13,15 +13,6 @@ ask on the `Mailing List <http://scikit-learn.org/stable/support.html>`_
Q&A communities with Machine Learning practitioners
----------------------------------------------------
- :Metaoptimize/QA:
-
- A forum for Machine Learning, Natural Language Processing and
- other Data Analytics discussions (similar to what Stackoverflow
- is for developers): http://metaoptimize.com/qa
-
- A good starting point is the discussion on `good freely available
- textbooks on machine learning`_
-
:Quora.com:
Quora has a topic for Machine Learning related questions that
@@ -30,13 +21,15 @@ Q&A communities with Machine Learning practitioners
Have a look at the best questions section, eg: `What are some
good resources for learning about machine learning`_.
+
+ :Stack Exchange:
-
-
-.. _`good freely available textbooks on machine learning`: http://metaoptimize.com/qa/questions/186/good-freely-available-textbooks-on-machine-learning
+ The Stack Exchange family of sites hosts `multiple subdomains for Machine Learning questions`_.
.. _`How do I learn machine learning?`: https://www.quora.com/How-do-I-learn-machine-learning-1
+.. _`multiple subdomains for Machine Learning questions`: http://meta.stackexchange.com/questions/130524/which-stack-exchange-website-for-machine-learning-and-computational-algorithms
+
-- _'An excellent free online course for Machine Learning taught by Professor Andrew Ng of Stanford': https://www.coursera.org/learn/machine-learning
-- _'Another excellent free online course that takes a more general approach to Artificial Intelligence': https://www.udacity.com/course/intro-to-artificial-intelligence--cs271
diff --git a/examples/neighbors/plot_species_kde.py b/examples/neighbors/plot_species_kde.py
index c582d76a9b..99452f29c2 100644
--- a/examples/neighbors/plot_species_kde.py
+++ b/examples/neighbors/plot_species_kde.py
@@ -23,7 +23,7 @@ The two species are:
the Brown-throated Sloth.
- `"Microryzomys minutus"
- <http://www.iucnredlist.org/apps/redlist/details/13408/0>`_ ,
+ <http://www.iucnredlist.org/details/13408/0>`_ ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.
diff --git a/sklearn/cluster/birch.py b/sklearn/cluster/birch.py
index 99df8d8287..05b618ddb8 100644
--- a/sklearn/cluster/birch.py
+++ b/sklearn/cluster/birch.py
@@ -401,7 +401,7 @@ class Birch(BaseEstimator, TransformerMixin, ClusterMixin):
* Roberto Perdisci
JBirch - Java implementation of BIRCH clustering algorithm
- https://code.google.com/p/jbirch/
+ https://code.google.com/archive/p/jbirch
"""
def __init__(self, threshold=0.5, branching_factor=50, n_clusters=3,
diff --git a/sklearn/datasets/olivetti_faces.py b/sklearn/datasets/olivetti_faces.py
index 978a00db3c..ba21bba64b 100644
--- a/sklearn/datasets/olivetti_faces.py
+++ b/sklearn/datasets/olivetti_faces.py
@@ -1,8 +1,8 @@
"""Modified Olivetti faces dataset.
-The original database was available from (now defunct)
+The original database was available from
- http://www.uk.research.att.com/facedatabase.html
+ http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
The version retrieved here comes in MATLAB format from the personal
web page of Sam Roweis:
@@ -98,7 +98,7 @@ def fetch_olivetti_faces(data_home=None, shuffle=False, random_state=0,
This dataset consists of 10 pictures each of 40 individuals. The original
database was available from (now defunct)
- http://www.uk.research.att.com/facedatabase.html
+ http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
The version retrieved here comes in MATLAB format from the personal
web page of Sam Roweis:
diff --git a/sklearn/datasets/species_distributions.py b/sklearn/datasets/species_distributions.py
index 3d10528e7b..2720aab6e1 100644
--- a/sklearn/datasets/species_distributions.py
+++ b/sklearn/datasets/species_distributions.py
@@ -9,11 +9,11 @@ The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
- <http://www.iucnredlist.org/apps/redlist/details/3038/0>`_ ,
+ <http://www.iucnredlist.org/details/3038/0>`_ ,
the Brown-throated Sloth.
- `"Microryzomys minutus"
- <http://www.iucnredlist.org/apps/redlist/details/13408/0>`_ ,
+ <http://www.iucnredlist.org/details/13408/0>`_ ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.
@@ -182,11 +182,11 @@ def fetch_species_distributions(data_home=None,
The two species are:
- `"Bradypus variegatus"
- <http://www.iucnredlist.org/apps/redlist/details/3038/0>`_ ,
+ <http://www.iucnredlist.org/details/3038/0>`_ ,
the Brown-throated Sloth.
- `"Microryzomys minutus"
- <http://www.iucnredlist.org/apps/redlist/details/13408/0>`_ ,
+ <http://www.iucnredlist.org/details/13408/0>`_ ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.
diff --git a/sklearn/decomposition/online_lda.py b/sklearn/decomposition/online_lda.py
index 44a1966501..bc6119742d 100644
--- a/sklearn/decomposition/online_lda.py
+++ b/sklearn/decomposition/online_lda.py
@@ -5,7 +5,7 @@ Online Latent Dirichlet Allocation with variational inference
=============================================================
This implementation is modified from Matthew D. Hoffman's onlineldavb code
-Link: http://www.cs.princeton.edu/~mdhoffma/code/onlineldavb.tar
+Link: http://matthewdhoffman.com/code/onlineldavb.tar
"""
# Author: Chyi-Kwei Yau
@@ -241,7 +241,7 @@ class LatentDirichletAllocation(BaseEstimator, TransformerMixin):
Chong Wang, John Paisley, 2013
[3] Matthew D. Hoffman's onlineldavb code. Link:
- http://www.cs.princeton.edu/~mdhoffma/code/onlineldavb.tar
+ http://matthewdhoffman.com//code/onlineldavb.tar
"""
diff --git a/sklearn/feature_selection/mutual_info_.py b/sklearn/feature_selection/mutual_info_.py
index 0b205c2011..463cc12e6d 100644
--- a/sklearn/feature_selection/mutual_info_.py
+++ b/sklearn/feature_selection/mutual_info_.py
@@ -349,7 +349,7 @@ def mutual_info_regression(X, y, discrete_features='auto', n_neighbors=3,
References
----------
- .. [1] `Mutual Information <http://en.wikipedia.org/wiki/Mutual_information>`_
+ .. [1] `Mutual Information <https://en.wikipedia.org/wiki/Mutual_information>`_
on Wikipedia.
.. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
information". Phys. Rev. E 69, 2004.
@@ -424,7 +424,7 @@ def mutual_info_classif(X, y, discrete_features='auto', n_neighbors=3,
References
----------
- .. [1] `Mutual Information <http://en.wikipedia.org/wiki/Mutual_information>`_
+ .. [1] `Mutual Information <https://en.wikipedia.org/wiki/Mutual_information>`_
on Wikipedia.
.. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
information". Phys. Rev. E 69, 2004.
diff --git a/sklearn/gaussian_process/gaussian_process.py b/sklearn/gaussian_process/gaussian_process.py
index 19a6820398..d61ad21bb8 100644
--- a/sklearn/gaussian_process/gaussian_process.py
+++ b/sklearn/gaussian_process/gaussian_process.py
@@ -208,7 +208,7 @@ class GaussianProcess(BaseEstimator, RegressorMixin):
.. [WBSWM1992] `W.J. Welch, R.J. Buck, J. Sacks, H.P. Wynn, T.J. Mitchell,
and M.D. Morris (1992). Screening, predicting, and computer
experiments. Technometrics, 34(1) 15--25.`
- http://www.jstor.org/pss/1269548
+ http://www.jstor.org/stable/1269548
"""
_regression_types = {
diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py
index de2c9be717..a47016e448 100644
--- a/sklearn/kernel_approximation.py
+++ b/sklearn/kernel_approximation.py
@@ -50,7 +50,7 @@ class RBFSampler(BaseEstimator, TransformerMixin):
[1] "Weighted Sums of Random Kitchen Sinks: Replacing
minimization with randomization in learning" by A. Rahimi and
Benjamin Recht.
- (http://www.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)
+ (http://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)
"""
def __init__(self, gamma=1., n_components=100, random_state=None):
diff --git a/sklearn/linear_model/least_angle.py b/sklearn/linear_model/least_angle.py
index 1434ae6526..f83741618a 100644
--- a/sklearn/linear_model/least_angle.py
+++ b/sklearn/linear_model/least_angle.py
@@ -141,7 +141,7 @@ def lars_path(X, y, Xy=None, Gram=None, max_iter=500,
<https://en.wikipedia.org/wiki/Least-angle_regression>`_
.. [3] `Wikipedia entry on the Lasso
- <https://en.wikipedia.org/wiki/Lasso_(statistics)#Lasso_method>`_
+ <https://en.wikipedia.org/wiki/Lasso_(statistics)>`_
"""
diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py
index 19235732e7..b06558f7d5 100644
--- a/sklearn/random_projection.py
+++ b/sklearn/random_projection.py
@@ -246,7 +246,7 @@ def sparse_random_matrix(n_components, n_features, density='auto',
.. [1] Ping Li, T. Hastie and K. W. Church, 2006,
"Very Sparse Random Projections".
- http://www.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
+ http://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
.. [2] D. Achlioptas, 2001, "Database-friendly random projections",
http://www.cs.ucsc.edu/~optas/papers/jl.pdf
@@ -581,7 +581,7 @@ class SparseRandomProjection(BaseRandomProjection):
.. [1] Ping Li, T. Hastie and K. W. Church, 2006,
"Very Sparse Random Projections".
- http://www.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
+ http://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
.. [2] D. Achlioptas, 2001, "Database-friendly random projections",
https://users.soe.ucsc.edu/~optas/papers/jl.pdf
--
GitLab